Apple Team Shared The Secret On How They Stay Ahead Of The Competition With A13 Chip

Aadhya Khatri - Sep 20, 2019


Apple Team Shared The Secret On How They Stay Ahead Of The Competition With A13 Chip

Phil Schiller and Anand Shimpi, two employees of Apple, shared their approach in the design of chips and how they improve the performance every year

Recently, Phil Schiller, Apple’s VP of product marketing, and Anand Shimpi, a member of the Platform Architecture team, shared their approach in the design of chips and how they manage to improve their performance every year.

In their interview, they pointed out how they made the chip to become more power efficient and what users can gain from Apple’s effort.

A13-bionic-chip-Apple
In their interview, they pointed out how they made the chip to become more power efficient and what users can gain from Apple’s effort

During the announcing event earlier this month, Apple shared some details regarding its A13 Bionic chip. It sports 8.5 billion transistors, two performance-focused cores, four efficiency-focused ones, making up a total of six cores, two machine learning accelerators, a quad-core GPU, and an octa-core neural engine. So the chip can work on a trillion operations each second.

So in comparison with the A12, the A13’s performance is 20% faster and its efficiency is increased by 30%. Many of Apple’s competitors now have eight cores in their chips, the iPhone maker’s smooth integration of software and hardware gives it a competitive edge over them all.

For an already high-performing chip to see such a significant boost is sort of like watching Usain Bolt beat himself in a sprint.

According to Anand Shimpi and Phil Schiller, their focus was efficiency when they develop the chip:

“We talk about performance a lot publicly,” Shimpi says, “but the reality is, we view it as performance per watt. We look at it as energy efficiency, and if you build an efficient design, you also happen to build a performance design.”

To shed some more light on the process of developing the chip, Schiller and Shimpi shared that CPU designs are guided by specific applications.

six-cores-A13-bionic-chip-apple
The A13 Bionic chip features six cores in total

Shimpi and Schiller both were forceful about this maniacal focus on power efficiency and performance. For instance, the CPU team will study how applications are being used on iOS, and then use the data to optimize future CPU designs. That way, when the next version of the device comes out, it will be better at doing the things that most people do on their iPhones.

Apps that do not require further optimization will use less energy than usual. The interview also uncovered that the iPhone maker makes use of the same approach to develop its machine learning and GPU.

This strategy isn’t just for CPUs. The same performance-per-watt rules apply to machine learning functions and graphics processing. For example, if a developer working on the iPhone’s camera software sees a lot of utilization of the GPU, then she can work with a GPU architect to figure out a better way of doing things. This leads to a more efficient design for future graphics chips.

The fact that the A13 has a different way of processing has made it stand out from other chips on the market.

Apple’s secret, though, lies in the way all of these various parts of the chip work together in a way that conserves battery power. In a typical smartphone chip, parts of the chip are turned on to do particular tasks. Think of it as turning on the power for an entire neighborhood for them to eat dinner and watch Game of Thrones, then turning the power off, then switching on the power for another neighborhood that wants to play videogames.

You can imagine the A13 to have one single home basis but with the usual on-and-off approach, which can help reduce the rate of electrodes going waste.

Schiller said that the key for all these optimizations was machine learning:

“Machine learning is running during all of that, whether it’s managing your battery life or optimizing performance,” Schiller said. “There wasn’t machine learning running ten years ago. Now, it’s always running, doing stuff.”

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